AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Logothetis, I.; Diakogiannis, K.; Vidakis, N.
Interactive Learning Through Conversational Avatars and Immersive VR: Enhancing Diabetes Education and Self-Management Proceedings Article
In: X., Fang (Ed.): Lect. Notes Comput. Sci., pp. 415–429, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303192577-1 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Chronic disease, Computer aided instruction, Diabetes Education, Diagnosis, E-Learning, Education management, Engineering education, Gamification, Immersive virtual reality, Interactive computer graphics, Interactive learning, Large population, Learning systems, NUI, Self management, Serious game, Serious games, simulation, Virtual Reality
@inproceedings{logothetis_interactive_2025,
title = {Interactive Learning Through Conversational Avatars and Immersive VR: Enhancing Diabetes Education and Self-Management},
author = {I. Logothetis and K. Diakogiannis and N. Vidakis},
editor = {Fang X.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105008266480&doi=10.1007%2f978-3-031-92578-8_27&partnerID=40&md5=451274dfa3ef0b3f1b39c7d5a665ee3b},
doi = {10.1007/978-3-031-92578-8_27},
isbn = {03029743 (ISSN); 978-303192577-1 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15816 LNCS},
pages = {415–429},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Diabetes is a chronic disease affecting a large population of the world. Education and self-management of diabetes are crucial. Technologies such as Virtual Reality (VR) have presented promising results in healthcare education, while studies suggest that Artificial Intelligence (AI) can help in learning by further engaging the learner. This study aims to educate users on the entire routine of managing diabetes. The serious game utilizes VR for realistic interaction with diabetes tools and generative AI through a conversational avatar that acts as an assistant instructor. In this way, it allows users to practice diagnostic and therapeutic interventions in a controlled virtual environment, helping to build their understanding and confidence in diabetes management. To measure the effects of the proposed serious game, presence, and perceived agency were measured. Preliminary results indicate that this setup aids in the engagement and immersion of learners, while the avatar can provide helpful information during gameplay. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Artificial intelligence, Chronic disease, Computer aided instruction, Diabetes Education, Diagnosis, E-Learning, Education management, Engineering education, Gamification, Immersive virtual reality, Interactive computer graphics, Interactive learning, Large population, Learning systems, NUI, Self management, Serious game, Serious games, simulation, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Diabetes is a chronic disease affecting a large population of the world. Education and self-management of diabetes are crucial. Technologies such as Virtual Reality (VR) have presented promising results in healthcare education, while studies suggest that Artificial Intelligence (AI) can help in learning by further engaging the learner. This study aims to educate users on the entire routine of managing diabetes. The serious game utilizes VR for realistic interaction with diabetes tools and generative AI through a conversational avatar that acts as an assistant instructor. In this way, it allows users to practice diagnostic and therapeutic interventions in a controlled virtual environment, helping to build their understanding and confidence in diabetes management. To measure the effects of the proposed serious game, presence, and perceived agency were measured. Preliminary results indicate that this setup aids in the engagement and immersion of learners, while the avatar can provide helpful information during gameplay. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Jayanthy, S.; Selvaganesh, M.; Kumar, S. Sakthi; Sathish, A. Manjunatha; Sabarisan, K. M.; Arasi, T. Senthamil
Generative AI Solution for CNC Machines and Robotics Code Generation Proceedings Article
In: Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331536695 (ISBN).
Abstract | Links | BibTeX | Tags: Adaptive control systems, Adversarial networks, Automated Code Generation, Automatic programming, CNC machine, CNC Machines, CNC system, Codegeneration, Computer aided instruction, Computer control, Computer control systems, E-Learning, Edge computing, Federated learning, Flow control, GANs, Generative pre-trained transformer transformer, GPT Transformers, Industrial research, Industry 4.0, Innovative approaches, Intelligent robots, Learning algorithms, Personnel training, Reinforcement Learning, Reinforcement learnings, Robotic systems, Simulation platform, Smart manufacturing, Virtual Reality
@inproceedings{jayanthy_generative_2025,
title = {Generative AI Solution for CNC Machines and Robotics Code Generation},
author = {S. Jayanthy and M. Selvaganesh and S. Sakthi Kumar and A. Manjunatha Sathish and K. M. Sabarisan and T. Senthamil Arasi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011963078&doi=10.1109%2FICCIES63851.2025.11033032&partnerID=40&md5=fb9143cd22dc48ae6c557f722cc2d6ab},
doi = {10.1109/ICCIES63851.2025.11033032},
isbn = {9798331536695 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The advent of Industry 4.0 has revolutionized the manufacturing landscape, driving significant advancements in automation and intelligence. This study introduces an innovative approach to automated code generation for CNC and robotic systems, leveraging Generative Adversarial Networks (GANs) and GPT(Generative Pre-trained Transformer) Transformers. These AI models enable precise and optimized code creation, minimizing manual errors. Adaptive process control, achieved through Reinforcement Learning (RL), allows real-time adjustments to operational parameters, enhancing performance in dynamic environments. The incorporation of natural language processing through Transformer models facilitates intuitive operator interactions via user-friendly interfaces. Immersive Virtual Reality (VR) technologies provide high-fidelity simulation and training platforms for realistic testing and control. Additionally, collaborative learning mechanisms, achieved through Federated Learning and Edge-cloud computing, support continuous improvement and scalable deployment. Impressive outcomes were attained by the system, including 90.5% training efficiency, 98.7% coding accuracy, 95.2% adaptability, and 93.4% operator satisfaction. Experimental results validate the system's superior accuracy, adaptability, and user-centric design, showcasing its potential to revolutionize manufacturing processes. This research sets a new benchmark for intelligent, efficient, and scalable automation in the Industry 4.0 era, paving the way for transformative innovations in smart manufacturing. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Adaptive control systems, Adversarial networks, Automated Code Generation, Automatic programming, CNC machine, CNC Machines, CNC system, Codegeneration, Computer aided instruction, Computer control, Computer control systems, E-Learning, Edge computing, Federated learning, Flow control, GANs, Generative pre-trained transformer transformer, GPT Transformers, Industrial research, Industry 4.0, Innovative approaches, Intelligent robots, Learning algorithms, Personnel training, Reinforcement Learning, Reinforcement learnings, Robotic systems, Simulation platform, Smart manufacturing, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of Industry 4.0 has revolutionized the manufacturing landscape, driving significant advancements in automation and intelligence. This study introduces an innovative approach to automated code generation for CNC and robotic systems, leveraging Generative Adversarial Networks (GANs) and GPT(Generative Pre-trained Transformer) Transformers. These AI models enable precise and optimized code creation, minimizing manual errors. Adaptive process control, achieved through Reinforcement Learning (RL), allows real-time adjustments to operational parameters, enhancing performance in dynamic environments. The incorporation of natural language processing through Transformer models facilitates intuitive operator interactions via user-friendly interfaces. Immersive Virtual Reality (VR) technologies provide high-fidelity simulation and training platforms for realistic testing and control. Additionally, collaborative learning mechanisms, achieved through Federated Learning and Edge-cloud computing, support continuous improvement and scalable deployment. Impressive outcomes were attained by the system, including 90.5% training efficiency, 98.7% coding accuracy, 95.2% adaptability, and 93.4% operator satisfaction. Experimental results validate the system's superior accuracy, adaptability, and user-centric design, showcasing its potential to revolutionize manufacturing processes. This research sets a new benchmark for intelligent, efficient, and scalable automation in the Industry 4.0 era, paving the way for transformative innovations in smart manufacturing. © 2025 Elsevier B.V., All rights reserved.
Anvitha, K.; Durjay, T.; Sathvika, K.; Gnanendra, G.; Annamalai, S.; Natarajan, S. K.
EduBot: A Compact AI-Driven Study Assistant for Contextual Knowledge Retrieval Proceedings Article
In: Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331507756 (ISBN).
Abstract | Links | BibTeX | Tags: Chatbots, Computer aided instruction, Contextual knowledge, Curricula, Digital Education, E-Learning, Education computing, Educational Technology, Engineering education, Indexing (of information), Information Retrieval, Intelligent systems, Knowledge retrieval, LangChain Framework, Language Model, Large language model, learning experience, Learning experiences, Learning systems, LLM, PDF - Driven Chatbot, Query processing, Students, Teaching, Traditional learning, Virtual Reality
@inproceedings{anvitha_edubot_2025,
title = {EduBot: A Compact AI-Driven Study Assistant for Contextual Knowledge Retrieval},
author = {K. Anvitha and T. Durjay and K. Sathvika and G. Gnanendra and S. Annamalai and S. K. Natarajan},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105013615976&doi=10.1109%2FGINOTECH63460.2025.11077097&partnerID=40&md5=b08377283f2ea2ee406d38d1d23f1e42},
doi = {10.1109/GINOTECH63460.2025.11077097},
isbn = {9798331507756 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In the evolving landscape of educational technology, intelligent systems are redefining traditional learning methods by enhancing accessibility, adaptability, and engagement in instructional processes. This paper presents EduBot, a PDF-Driven Chatbot developed using advanced Large Language Models (LLMs) and leveraging frameworks like LangChain, OpenAI's Chat-Gpt, and Pinecone. EduBot is designed as an interactive educational assistant, responding to student queries based on faculty-provided guidelines embedded in PDF documents. Through natural language processing, EduBot streamlines information retrieval, providing accurate, context-aware responses that foster a self- directed learning experience. By aligning with specific academic requirements and enhancing clarity in information delivery, EduBot stands as a promising tool in personalized digital learning support. This paper explores the design, implementation, and impact of EduBot, offering insights into its potential as a scalable solution for academic institutions The demand for accessible and adaptive educational tools is increasing as students seek more personalized and efficient ways to enhance their learning experience. EduBot is a cutting- edge PDF-driven chatbot designed to act as a virtual educational assistant, helping students to navigate and understand course materials by answering queries directly based on faculty guidelines. Built upon Large Language Models (LLMs), specifically utilizing frameworks such as LangChain and OpenAI's GPT-3.5, EduBot provides a sophisticated solution for integrating curated academic content into interactive learning. With its backend support from Pinecone for optimized data indexing, EduBot offers accurate and context-specific responses, facilitating a deeper level of engagement and comprehension. The average relevancy score is 80%. This paper outlines the design and deployment of EduBot, emphasizing its architecture, adaptability, and contributions to the educational landscape, where such AI- driven tools are poised to become indispensable in fostering autonomous, personalized learning environments. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Chatbots, Computer aided instruction, Contextual knowledge, Curricula, Digital Education, E-Learning, Education computing, Educational Technology, Engineering education, Indexing (of information), Information Retrieval, Intelligent systems, Knowledge retrieval, LangChain Framework, Language Model, Large language model, learning experience, Learning experiences, Learning systems, LLM, PDF - Driven Chatbot, Query processing, Students, Teaching, Traditional learning, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
In the evolving landscape of educational technology, intelligent systems are redefining traditional learning methods by enhancing accessibility, adaptability, and engagement in instructional processes. This paper presents EduBot, a PDF-Driven Chatbot developed using advanced Large Language Models (LLMs) and leveraging frameworks like LangChain, OpenAI's Chat-Gpt, and Pinecone. EduBot is designed as an interactive educational assistant, responding to student queries based on faculty-provided guidelines embedded in PDF documents. Through natural language processing, EduBot streamlines information retrieval, providing accurate, context-aware responses that foster a self- directed learning experience. By aligning with specific academic requirements and enhancing clarity in information delivery, EduBot stands as a promising tool in personalized digital learning support. This paper explores the design, implementation, and impact of EduBot, offering insights into its potential as a scalable solution for academic institutions The demand for accessible and adaptive educational tools is increasing as students seek more personalized and efficient ways to enhance their learning experience. EduBot is a cutting- edge PDF-driven chatbot designed to act as a virtual educational assistant, helping students to navigate and understand course materials by answering queries directly based on faculty guidelines. Built upon Large Language Models (LLMs), specifically utilizing frameworks such as LangChain and OpenAI's GPT-3.5, EduBot provides a sophisticated solution for integrating curated academic content into interactive learning. With its backend support from Pinecone for optimized data indexing, EduBot offers accurate and context-specific responses, facilitating a deeper level of engagement and comprehension. The average relevancy score is 80%. This paper outlines the design and deployment of EduBot, emphasizing its architecture, adaptability, and contributions to the educational landscape, where such AI- driven tools are poised to become indispensable in fostering autonomous, personalized learning environments. © 2025 Elsevier B.V., All rights reserved.
Boubakri, F. -E.; Kadri, M.; Kaghat, F. Z.; Azough, A.; Tairi, H.
Exploring 3D Cardiac Anatomy with Text-Based AI Guidance in Virtual Reality Proceedings Article
In: pp. 43–48, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331534899 (ISBN).
Abstract | Links | BibTeX | Tags: 3D cardiac anatomy, 3d heart models, Anatomy education, Anatomy educations, Cardiac anatomy, Collaborative environments, Collaborative learning, Computer aided instruction, Curricula, Design and Development, E-Learning, Education computing, Generative AI, Heart, Immersive environment, Learning systems, Natural language processing systems, Social virtual reality, Students, Teaching, Three dimensional computer graphics, Virtual Reality
@inproceedings{boubakri_exploring_2025,
title = {Exploring 3D Cardiac Anatomy with Text-Based AI Guidance in Virtual Reality},
author = {F. -E. Boubakri and M. Kadri and F. Z. Kaghat and A. Azough and H. Tairi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015676741&doi=10.1109%2FSCME62582.2025.11104869&partnerID=40&md5=c961694f97c50adc23b6826dddb265cd},
doi = {10.1109/SCME62582.2025.11104869},
isbn = {9798331534899 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {43–48},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This paper presents the design and development of a social virtual reality (VR) classroom focused on cardiac anatomy education for students in grades K-12. The application allows multiple learners to explore a detailed 3D heart model within an immersive and collaborative environment. A crucial part of the system is the integration of a text-based conversational AI interface powered by ChatGPT, which provides immediate, interactive explanations and addresses student inquiries about heart anatomy. The system supports both guided and exploratory learning modes, encourages peer collaboration, and offers personalized support through natural language dialogue. We evaluated the system's effectiveness through a comprehensive study measuring learning perception (LPQ), VR perception (VRPQ), AI perception (AIPQ), and VR-related symptoms (VRSQ). Potential applications include making high-quality cardiac anatomy education more affordable for K-12 schools with limited resources, offering an adaptable AI-based tutoring system for students to learn at their own pace, and equipping educators with an easy-to-use tool to integrate into their science curriculum with minimal additional training. © 2025 Elsevier B.V., All rights reserved.},
keywords = {3D cardiac anatomy, 3d heart models, Anatomy education, Anatomy educations, Cardiac anatomy, Collaborative environments, Collaborative learning, Computer aided instruction, Curricula, Design and Development, E-Learning, Education computing, Generative AI, Heart, Immersive environment, Learning systems, Natural language processing systems, Social virtual reality, Students, Teaching, Three dimensional computer graphics, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents the design and development of a social virtual reality (VR) classroom focused on cardiac anatomy education for students in grades K-12. The application allows multiple learners to explore a detailed 3D heart model within an immersive and collaborative environment. A crucial part of the system is the integration of a text-based conversational AI interface powered by ChatGPT, which provides immediate, interactive explanations and addresses student inquiries about heart anatomy. The system supports both guided and exploratory learning modes, encourages peer collaboration, and offers personalized support through natural language dialogue. We evaluated the system's effectiveness through a comprehensive study measuring learning perception (LPQ), VR perception (VRPQ), AI perception (AIPQ), and VR-related symptoms (VRSQ). Potential applications include making high-quality cardiac anatomy education more affordable for K-12 schools with limited resources, offering an adaptable AI-based tutoring system for students to learn at their own pace, and equipping educators with an easy-to-use tool to integrate into their science curriculum with minimal additional training. © 2025 Elsevier B.V., All rights reserved.
Chen, Y.; Yan, Y.; Yang, G.
Bringing Microbiology to Life in Museum: Using Mobile VR and LLM-Powered Virtual Character for Children's Science Learning Proceedings Article
In: Chui, K. T.; Jaikaeo, C.; Niramitranon, J.; Kaewmanee, W.; Ng, K. -K.; Ongkunaruk, P. (Ed.): pp. 83–87, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331595500 (ISBN).
Abstract | Links | BibTeX | Tags: Computer aided instruction, E-Learning, Engineering education, Experimental groups, Immersive technologies, Informal learning, Language Model, Large language model, large language models, Learning systems, Microbiology, Mobile virtual reality, Museum, Museums, Science education, Science learning, Virtual addresses, Virtual character, Virtual Reality, Virtual reality system
@inproceedings{chen_bringing_2025,
title = {Bringing Microbiology to Life in Museum: Using Mobile VR and LLM-Powered Virtual Character for Children's Science Learning},
author = {Y. Chen and Y. Yan and G. Yang},
editor = {K. T. Chui and C. Jaikaeo and J. Niramitranon and W. Kaewmanee and K. -K. Ng and P. Ongkunaruk},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015708152&doi=10.1109%2FISET65607.2025.00025&partnerID=40&md5=77ae9a4829656155010abc280a817a72},
doi = {10.1109/ISET65607.2025.00025},
isbn = {9798331595500 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {83–87},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Although the increasing advantages of immersive technology-enhanced museum informal learning in children's science education, the application of mobile virtual reality (MVR) technology combined with large language models (LLM) in this environment has not yet been fully explored. Furthermore, virtual character, as an intelligent learning assistant, is capable of providing personalized guidance and instant feedback to children through natural language interactions, but its potential in museum learning has yet to be fully tapped. To address these gaps, this study investigates the effectiveness of integrating MVR with LLM-powered virtual character in promoting children's microbiology learning during museum activities. In this paper, the technology-enhanced POE (Prediction-observation-explanation) learning model was studied, and the corresponding MVR system was designed and developed to carry out microbial learning activities. A quasiexperimental design was used with 60 children aged 10-12. The experimental group learned via an MVR system combining LLM-powered virtual character, while the control group used traditional methods. Results showed the experimental group significantly outperformed the control group in both academic achievement and learning motivation, including attention, confidence, and satisfaction. This provides evidence for using immersive technologies in informal learning and offers insights into applying LLM-powered virtual character in science education. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Computer aided instruction, E-Learning, Engineering education, Experimental groups, Immersive technologies, Informal learning, Language Model, Large language model, large language models, Learning systems, Microbiology, Mobile virtual reality, Museum, Museums, Science education, Science learning, Virtual addresses, Virtual character, Virtual Reality, Virtual reality system},
pubstate = {published},
tppubtype = {inproceedings}
}
Although the increasing advantages of immersive technology-enhanced museum informal learning in children's science education, the application of mobile virtual reality (MVR) technology combined with large language models (LLM) in this environment has not yet been fully explored. Furthermore, virtual character, as an intelligent learning assistant, is capable of providing personalized guidance and instant feedback to children through natural language interactions, but its potential in museum learning has yet to be fully tapped. To address these gaps, this study investigates the effectiveness of integrating MVR with LLM-powered virtual character in promoting children's microbiology learning during museum activities. In this paper, the technology-enhanced POE (Prediction-observation-explanation) learning model was studied, and the corresponding MVR system was designed and developed to carry out microbial learning activities. A quasiexperimental design was used with 60 children aged 10-12. The experimental group learned via an MVR system combining LLM-powered virtual character, while the control group used traditional methods. Results showed the experimental group significantly outperformed the control group in both academic achievement and learning motivation, including attention, confidence, and satisfaction. This provides evidence for using immersive technologies in informal learning and offers insights into applying LLM-powered virtual character in science education. © 2025 Elsevier B.V., All rights reserved.
Vadisetty, R.; Polamarasetti, A.; Goyal, M. K.; Rongali, S. K.; Prajapati, S. K.; Butani, J. B.
Generative AI for Creating Immersive Learning Environments: Virtual Reality and Beyond Proceedings Article
In: Mishra, S.; Tripathy, H. K.; Mohanty, J. R. (Ed.): Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331523022 (ISBN).
Abstract | Links | BibTeX | Tags: AI in Education, Artificial intelligence in education, Augmented Reality, Augmented Reality (AR), Computer aided instruction, E-Learning, Educational spaces, Generative adversarial networks, Generative AI, generative artificial intelligence, Immersive, Immersive learning, Learning Environments, Learning systems, Personalized learning, Virtual and augmented reality, Virtual environments, Virtual Reality, Virtual Reality (VR)
@inproceedings{vadisetty_generative_2025,
title = {Generative AI for Creating Immersive Learning Environments: Virtual Reality and Beyond},
author = {R. Vadisetty and A. Polamarasetti and M. K. Goyal and S. K. Rongali and S. K. Prajapati and J. B. Butani},
editor = {S. Mishra and H. K. Tripathy and J. R. Mohanty},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018128093&doi=10.1109%2FASSIC64892.2025.11158626&partnerID=40&md5=b29a005f42262bf50c58d7708e2ed91a},
doi = {10.1109/ASSIC64892.2025.11158626},
isbn = {9798331523022 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Generative Artificial Intelligence (AI) revolutionizes immersive educational spaces with dynamic, personalized, and interactive experiences. In this article, Generative AI addresses its role in Virtual and Augmented Realities through automated creation, personalized learning pathways, and heightened engagement. With Generative AI, educational simulations can adapt to learner performance, produce interactive characters, and present real-time feedback through models such as Generative Adversarial Networks (GANs) and Transformerbased AI. Considering its potential, computational limitations, ethics, and authentic content concerns must be considered. In its examination, current implementations, benefits, and impediments, such as AI-powered flexible learning, are discussed in detail in this work. In conclusion, Generative AI's role in changing immersive instruction and opening doors for amplified and augmented educational offerings is stressed. © 2025 Elsevier B.V., All rights reserved.},
keywords = {AI in Education, Artificial intelligence in education, Augmented Reality, Augmented Reality (AR), Computer aided instruction, E-Learning, Educational spaces, Generative adversarial networks, Generative AI, generative artificial intelligence, Immersive, Immersive learning, Learning Environments, Learning systems, Personalized learning, Virtual and augmented reality, Virtual environments, Virtual Reality, Virtual Reality (VR)},
pubstate = {published},
tppubtype = {inproceedings}
}
Generative Artificial Intelligence (AI) revolutionizes immersive educational spaces with dynamic, personalized, and interactive experiences. In this article, Generative AI addresses its role in Virtual and Augmented Realities through automated creation, personalized learning pathways, and heightened engagement. With Generative AI, educational simulations can adapt to learner performance, produce interactive characters, and present real-time feedback through models such as Generative Adversarial Networks (GANs) and Transformerbased AI. Considering its potential, computational limitations, ethics, and authentic content concerns must be considered. In its examination, current implementations, benefits, and impediments, such as AI-powered flexible learning, are discussed in detail in this work. In conclusion, Generative AI's role in changing immersive instruction and opening doors for amplified and augmented educational offerings is stressed. © 2025 Elsevier B.V., All rights reserved.
Lin, J.; Wang, J.; Feng, P.; Zhang, X.; Yu, D.; Zhang, J.
AI-aided Automated AR-Assisted Assembly Instruction Authoring and Generation method Journal Article
In: Journal of Manufacturing Systems, vol. 83, pp. 405–423, 2025, ISSN: 02786125 (ISSN), (Publisher: Elsevier B.V.).
Abstract | Links | BibTeX | Tags: Ai-aided, Assembly, Assembly instructions, Assembly system, Assembly systems, Augmented Reality, Automatic programming, Computer aided instruction, Computer interaction, Generation method, Hand manipulation, Human computer interaction, human–computer interaction, Industrial assemblies, Intelligent method, Point cloud, Point-clouds, Real- time, Virtual Reality
@article{lin_ai-aided_2025,
title = {AI-aided Automated AR-Assisted Assembly Instruction Authoring and Generation method},
author = {J. Lin and J. Wang and P. Feng and X. Zhang and D. Yu and J. Zhang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017229936&doi=10.1016%2Fj.jmsy.2025.08.019&partnerID=40&md5=7957487b03f997dce9b6600e75481319},
doi = {10.1016/j.jmsy.2025.08.019},
issn = {02786125 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Journal of Manufacturing Systems},
volume = {83},
pages = {405–423},
abstract = {While Augmented Reality (AR) offers the potential to provide real-time guidance, one of the barriers to its adoption in industrial assembly is the lack of fast, no-code, intelligent methods for generating AR-assisted assembly programs. This paper proposes an AI-aided AR-Assisted Assembly Instruction Authoring and Generation method (ARAIAG) to address these challenges. ARAIAG allows engineers, without coding expertise, to intuitively design AR-assisted assembly instructions based on assembly demonstrations captured through RGBD cameras. Based on ARAIAG, we propose a new algorithm considering hand manipulation and model characteristics to achieve spatial registration for models, virtual-physical fusion, and assembly direction recognition. Additionally, we employed a novel human–computer interaction method and Large Language Model (LLM)-assisted content generation to achieve the automatic creation of interactive and instructive AR-assisted assembly programs. Through this approach, we streamline program development and enable more efficient AR-assisted assembly in dynamic manufacturing environments. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Elsevier B.V.},
keywords = {Ai-aided, Assembly, Assembly instructions, Assembly system, Assembly systems, Augmented Reality, Automatic programming, Computer aided instruction, Computer interaction, Generation method, Hand manipulation, Human computer interaction, human–computer interaction, Industrial assemblies, Intelligent method, Point cloud, Point-clouds, Real- time, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
While Augmented Reality (AR) offers the potential to provide real-time guidance, one of the barriers to its adoption in industrial assembly is the lack of fast, no-code, intelligent methods for generating AR-assisted assembly programs. This paper proposes an AI-aided AR-Assisted Assembly Instruction Authoring and Generation method (ARAIAG) to address these challenges. ARAIAG allows engineers, without coding expertise, to intuitively design AR-assisted assembly instructions based on assembly demonstrations captured through RGBD cameras. Based on ARAIAG, we propose a new algorithm considering hand manipulation and model characteristics to achieve spatial registration for models, virtual-physical fusion, and assembly direction recognition. Additionally, we employed a novel human–computer interaction method and Large Language Model (LLM)-assisted content generation to achieve the automatic creation of interactive and instructive AR-assisted assembly programs. Through this approach, we streamline program development and enable more efficient AR-assisted assembly in dynamic manufacturing environments. © 2025 Elsevier B.V., All rights reserved.
2024
Krauss, C.; Bassbouss, L.; Upravitelev, M.; An, T. -S.; Altun, D.; Reray, L.; Balitzki, E.; Tamimi, T. El; Karagülle, M.
Opportunities and Challenges in Developing Educational AI-Assistants for the Metaverse Proceedings Article
In: R.A., Sottilare; J., Schwarz (Ed.): Lect. Notes Comput. Sci., pp. 219–238, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303160608-3 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, AI-assistant, AI-Assistants, Computational Linguistics, Computer aided instruction, Concept-based, E-Learning, Education, Interoperability, Language Model, Large language model, large language models, Learning Environments, Learning systems, Learning Technologies, Learning technology, LLM, Metaverse, Metaverses, Natural language processing systems, Proof of concept, User interfaces, Virtual assistants, Virtual Reality
@inproceedings{krauss_opportunities_2024,
title = {Opportunities and Challenges in Developing Educational AI-Assistants for the Metaverse},
author = {C. Krauss and L. Bassbouss and M. Upravitelev and T. -S. An and D. Altun and L. Reray and E. Balitzki and T. El Tamimi and M. Karagülle},
editor = {Sottilare R.A. and Schwarz J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196214138&doi=10.1007%2f978-3-031-60609-0_16&partnerID=40&md5=9a66876cb30e9e5d287a86e6cfa66e05},
doi = {10.1007/978-3-031-60609-0_16},
isbn = {03029743 (ISSN); 978-303160608-3 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {14727 LNCS},
pages = {219–238},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The paper explores the opportunities and challenges for metaverse learning environments with AI-Assistants based on Large Language Models. A proof of concept based on popular but proprietary technologies is presented that enables a natural language exchange between the user and an AI-based medical expert in a highly immersive environment based on the Unreal Engine. The answers generated by ChatGPT are not only played back lip-synchronously, but also visualized in the VR environment using a 3D model of a skeleton. Usability and user experience play a particularly important role in the development of the highly immersive AI-Assistant. The proof of concept serves to illustrate the opportunities and challenges that lie in the merging of large language models, metaverse applications and educational ecosystems, which are self-contained research areas. Development strategies, tools and interoperability standards will be presented to facilitate future developments in this triangle of tension. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {3D modeling, AI-assistant, AI-Assistants, Computational Linguistics, Computer aided instruction, Concept-based, E-Learning, Education, Interoperability, Language Model, Large language model, large language models, Learning Environments, Learning systems, Learning Technologies, Learning technology, LLM, Metaverse, Metaverses, Natural language processing systems, Proof of concept, User interfaces, Virtual assistants, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper explores the opportunities and challenges for metaverse learning environments with AI-Assistants based on Large Language Models. A proof of concept based on popular but proprietary technologies is presented that enables a natural language exchange between the user and an AI-based medical expert in a highly immersive environment based on the Unreal Engine. The answers generated by ChatGPT are not only played back lip-synchronously, but also visualized in the VR environment using a 3D model of a skeleton. Usability and user experience play a particularly important role in the development of the highly immersive AI-Assistant. The proof of concept serves to illustrate the opportunities and challenges that lie in the merging of large language models, metaverse applications and educational ecosystems, which are self-contained research areas. Development strategies, tools and interoperability standards will be presented to facilitate future developments in this triangle of tension. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Williams, R.
Deep HoriXons - 3D Virtual Generative AI Assisted Campus for Deep Learning AI and Cybersecurity Proceedings Article
In: M., Blowers; B.T., Wysocki (Ed.): Proc SPIE Int Soc Opt Eng, SPIE, 2024, ISBN: 0277786X (ISSN); 978-151067434-9 (ISBN).
Abstract | Links | BibTeX | Tags: 3D virtual campus, AI and cybersecurity education, AI talent pipeline, ChatGPT digital tutor, CompTIA Security+, Computer aided instruction, Cyber security, Cyber-security educations, Cybersecurity, Deep learning, E-Learning, Immersive, Learning systems, Virtual campus, Virtual learning environments, Virtual Reality
@inproceedings{williams_deep_2024,
title = {Deep HoriXons - 3D Virtual Generative AI Assisted Campus for Deep Learning AI and Cybersecurity},
author = {R. Williams},
editor = {Blowers M. and Wysocki B.T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196555361&doi=10.1117%2f12.3011374&partnerID=40&md5=ff7392a37a51044c79d4d2824c9cf46b},
doi = {10.1117/12.3011374},
isbn = {0277786X (ISSN); 978-151067434-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc SPIE Int Soc Opt Eng},
volume = {13058},
publisher = {SPIE},
abstract = {This abstract outlines two significant innovations in AI and cybersecurity education within the "Deep HoriXons" 3D virtual campus, addressing the urgent need for skilled professionals in these domains. First, the paper introduces "Deep HoriXons," an immersive 3D virtual learning environment designed to democratize and enhance the educational experience for AI and cybersecurity. This innovation is notable for its global accessibility and ability to simulate real-world scenarios, providing an interactive platform for experiential learning, which is a marked departure from traditional educational models. The second innovation discussed is the strategic integration of ChatGPT as a digital educator and tutor within this virtual environment. ChatGPT's role is pivotal in offering tailored, real-time educational support, making complex AI and cybersecurity concepts more accessible and engaging for learners. This application of ChatGPT is an innovation worth noting for its ability to adapt to individual learning styles, provide interactive scenario-based learning, and support a deeper understanding of technical subjects through dynamic, responsive interaction. Together, these innovations represent a significant advancement in the field of AI and cybersecurity education, addressing the critical talent shortage by making high-quality, interactive learning experiences accessible on a global scale. The paper highlights the importance of these innovations in creating a skilled workforce capable of tackling the evolving challenges in AI and cybersecurity, underscoring the need for ongoing research and development in this area. © 2024 SPIE.},
keywords = {3D virtual campus, AI and cybersecurity education, AI talent pipeline, ChatGPT digital tutor, CompTIA Security+, Computer aided instruction, Cyber security, Cyber-security educations, Cybersecurity, Deep learning, E-Learning, Immersive, Learning systems, Virtual campus, Virtual learning environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This abstract outlines two significant innovations in AI and cybersecurity education within the "Deep HoriXons" 3D virtual campus, addressing the urgent need for skilled professionals in these domains. First, the paper introduces "Deep HoriXons," an immersive 3D virtual learning environment designed to democratize and enhance the educational experience for AI and cybersecurity. This innovation is notable for its global accessibility and ability to simulate real-world scenarios, providing an interactive platform for experiential learning, which is a marked departure from traditional educational models. The second innovation discussed is the strategic integration of ChatGPT as a digital educator and tutor within this virtual environment. ChatGPT's role is pivotal in offering tailored, real-time educational support, making complex AI and cybersecurity concepts more accessible and engaging for learners. This application of ChatGPT is an innovation worth noting for its ability to adapt to individual learning styles, provide interactive scenario-based learning, and support a deeper understanding of technical subjects through dynamic, responsive interaction. Together, these innovations represent a significant advancement in the field of AI and cybersecurity education, addressing the critical talent shortage by making high-quality, interactive learning experiences accessible on a global scale. The paper highlights the importance of these innovations in creating a skilled workforce capable of tackling the evolving challenges in AI and cybersecurity, underscoring the need for ongoing research and development in this area. © 2024 SPIE.
Sarshartehrani, F.; Mohammadrezaei, E.; Behravan, M.; Gracanin, D.
Enhancing E-Learning Experience Through Embodied AI Tutors in Immersive Virtual Environments: A Multifaceted Approach for Personalized Educational Adaptation Proceedings Article
In: R.A., Sottilare; J., Schwarz (Ed.): Lect. Notes Comput. Sci., pp. 272–287, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303160608-3 (ISBN).
Abstract | Links | BibTeX | Tags: Adaptive Learning, Artificial intelligence, Artificial intelligence in education, Computer aided instruction, Computer programming, E - learning, E-Learning, Education computing, Embodied artificial intelligence, Engineering education, Immersive Virtual Environments, Learner Engagement, Learning experiences, Learning systems, Multi-faceted approach, Personalized Instruction, Traditional boundaries, Virtual Reality
@inproceedings{sarshartehrani_enhancing_2024,
title = {Enhancing E-Learning Experience Through Embodied AI Tutors in Immersive Virtual Environments: A Multifaceted Approach for Personalized Educational Adaptation},
author = {F. Sarshartehrani and E. Mohammadrezaei and M. Behravan and D. Gracanin},
editor = {Sottilare R.A. and Schwarz J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196174389&doi=10.1007%2f978-3-031-60609-0_20&partnerID=40&md5=3801d0959781b1a191a3eb14f47bd8d8},
doi = {10.1007/978-3-031-60609-0_20},
isbn = {03029743 (ISSN); 978-303160608-3 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {14727 LNCS},
pages = {272–287},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {As digital education transcends traditional boundaries, e-learning experiences are increasingly shaped by cutting-edge technologies like artificial intelligence (AI), virtual reality (VR), and adaptive learning systems. This study examines the integration of AI-driven personalized instruction within immersive VR environments, targeting enhanced learner engagement-a core metric in online education effectiveness. Employing a user-centric design, the research utilizes embodied AI tutors, calibrated to individual learners’ emotional intelligence and cognitive states, within a Python programming curriculum-a key area in computer science education. The methodology relies on intelligent tutoring systems and personalized learning pathways, catering to a diverse participant pool from Virginia Tech. Our data-driven approach, underpinned by the principles of educational psychology and computational pedagogy, indicates that AI-enhanced virtual learning environments significantly elevate user engagement and proficiency in programming education. Although the scope is limited to a single academic institution, the promising results advocate for the scalability of such AI-powered educational tools, with potential implications for distance learning, MOOCs, and lifelong learning platforms. This research contributes to the evolving narrative of smart education and the role of large language models (LLMs) in crafting bespoke educational experiences, suggesting a paradigm shift towards more interactive, personalized e-learning solutions that align with global educational technology trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Adaptive Learning, Artificial intelligence, Artificial intelligence in education, Computer aided instruction, Computer programming, E - learning, E-Learning, Education computing, Embodied artificial intelligence, Engineering education, Immersive Virtual Environments, Learner Engagement, Learning experiences, Learning systems, Multi-faceted approach, Personalized Instruction, Traditional boundaries, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
As digital education transcends traditional boundaries, e-learning experiences are increasingly shaped by cutting-edge technologies like artificial intelligence (AI), virtual reality (VR), and adaptive learning systems. This study examines the integration of AI-driven personalized instruction within immersive VR environments, targeting enhanced learner engagement-a core metric in online education effectiveness. Employing a user-centric design, the research utilizes embodied AI tutors, calibrated to individual learners’ emotional intelligence and cognitive states, within a Python programming curriculum-a key area in computer science education. The methodology relies on intelligent tutoring systems and personalized learning pathways, catering to a diverse participant pool from Virginia Tech. Our data-driven approach, underpinned by the principles of educational psychology and computational pedagogy, indicates that AI-enhanced virtual learning environments significantly elevate user engagement and proficiency in programming education. Although the scope is limited to a single academic institution, the promising results advocate for the scalability of such AI-powered educational tools, with potential implications for distance learning, MOOCs, and lifelong learning platforms. This research contributes to the evolving narrative of smart education and the role of large language models (LLMs) in crafting bespoke educational experiences, suggesting a paradigm shift towards more interactive, personalized e-learning solutions that align with global educational technology trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Pester, A.; Tammaa, A.; Gütl, C.; Steinmaurer, A.; El-Seoud, S. A.
Conversational Agents, Virtual Worlds, and Beyond: A Review of Large Language Models Enabling Immersive Learning Proceedings Article
In: IEEE Global Eng. Edu. Conf., EDUCON, IEEE Computer Society, 2024, ISBN: 21659559 (ISSN); 979-835039402-3 (ISBN).
Abstract | Links | BibTeX | Tags: Computational Linguistics, Computer aided instruction, Conversational Agents, Education, Immersive learning, Language Model, Large language model, Learning systems, Literature reviews, LLM, Metaverse, Metaverses, Natural language processing systems, Pedagogy, Survey literature review, Virtual Reality, Virtual worlds
@inproceedings{pester_conversational_2024,
title = {Conversational Agents, Virtual Worlds, and Beyond: A Review of Large Language Models Enabling Immersive Learning},
author = {A. Pester and A. Tammaa and C. Gütl and A. Steinmaurer and S. A. El-Seoud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199068668&doi=10.1109%2fEDUCON60312.2024.10578895&partnerID=40&md5=1b904fd8a5e06d7ced42a328c028bbb7},
doi = {10.1109/EDUCON60312.2024.10578895},
isbn = {21659559 (ISSN); 979-835039402-3 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Global Eng. Edu. Conf., EDUCON},
publisher = {IEEE Computer Society},
abstract = {Large Language Models represent a significant breakthrough in Natural Language Processing research and opened a wide range of application domains. This paper demonstrates the successful integration of Large Language Models into immersive learning environments. The review highlights how this emerging technology aligns with pedagogical principles, enhancing the effectiveness of current educational systems. It also reflects recent advancements in integrating Large Language Models, including fine-tuning, hallucination reduction, fact-checking, and human evaluation of generated results. © 2024 IEEE.},
keywords = {Computational Linguistics, Computer aided instruction, Conversational Agents, Education, Immersive learning, Language Model, Large language model, Learning systems, Literature reviews, LLM, Metaverse, Metaverses, Natural language processing systems, Pedagogy, Survey literature review, Virtual Reality, Virtual worlds},
pubstate = {published},
tppubtype = {inproceedings}
}
Large Language Models represent a significant breakthrough in Natural Language Processing research and opened a wide range of application domains. This paper demonstrates the successful integration of Large Language Models into immersive learning environments. The review highlights how this emerging technology aligns with pedagogical principles, enhancing the effectiveness of current educational systems. It also reflects recent advancements in integrating Large Language Models, including fine-tuning, hallucination reduction, fact-checking, and human evaluation of generated results. © 2024 IEEE.
Gaudi, T.; Kapralos, B.; Uribe, A.
Structural and Functional Fidelity of Virtual Humans in Immersive Virtual Learning Environments Proceedings Article
In: IEEE Gaming, Entertain., Media Conf., GEM, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 9798350374537 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, Computer aided instruction, Digital representations, E-Learning, Engagement, fidelity, Immersive, Immersive virtual learning environment, Serious game, Serious games, Three dimensional computer graphics, Virtual character, virtual human, Virtual humans, Virtual instructors, Virtual learning environments, Virtual Reality, virtual simulation, Virtual simulations
@inproceedings{gaudi_structural_2024,
title = {Structural and Functional Fidelity of Virtual Humans in Immersive Virtual Learning Environments},
author = {T. Gaudi and B. Kapralos and A. Uribe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199517136&doi=10.1109%2FGEM61861.2024.10585535&partnerID=40&md5=3fe8f64b88ce17b50f34f458ab5a59fc},
doi = {10.1109/GEM61861.2024.10585535},
isbn = {9798350374537 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Gaming, Entertain., Media Conf., GEM},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Central to many immersive virtual learning environments (iVLEs) are virtual humans, or characters that are digital representations, which can serve as virtual instructors to facilitate learning. Current technology is allowing the production of photo-realistic (high fidelity/highly realistic) avatars, whether using traditional approaches relying on 3D modeling, or modern tools leveraging generative AI and virtual character creation tools. However, fidelity (i.e., level of realism) is complex as it can be analyzed from various points of view referring to its structure, function, interactivity, and behavior among others. Given its relevance, fidelity can influence various aspects of iVLEs including engagement and ultimately learning outcomes. In this work-in-progress paper, we propose a study that will examine the effect of structural and functional fidelity of a virtual human assistant on engagement within a virtual simulation designed to teach the cognitive aspects (e.g., the steps of a procedure) of the heart auscultation procedure. © 2024 Elsevier B.V., All rights reserved.},
keywords = {3D modeling, Computer aided instruction, Digital representations, E-Learning, Engagement, fidelity, Immersive, Immersive virtual learning environment, Serious game, Serious games, Three dimensional computer graphics, Virtual character, virtual human, Virtual humans, Virtual instructors, Virtual learning environments, Virtual Reality, virtual simulation, Virtual simulations},
pubstate = {published},
tppubtype = {inproceedings}
}
Central to many immersive virtual learning environments (iVLEs) are virtual humans, or characters that are digital representations, which can serve as virtual instructors to facilitate learning. Current technology is allowing the production of photo-realistic (high fidelity/highly realistic) avatars, whether using traditional approaches relying on 3D modeling, or modern tools leveraging generative AI and virtual character creation tools. However, fidelity (i.e., level of realism) is complex as it can be analyzed from various points of view referring to its structure, function, interactivity, and behavior among others. Given its relevance, fidelity can influence various aspects of iVLEs including engagement and ultimately learning outcomes. In this work-in-progress paper, we propose a study that will examine the effect of structural and functional fidelity of a virtual human assistant on engagement within a virtual simulation designed to teach the cognitive aspects (e.g., the steps of a procedure) of the heart auscultation procedure. © 2024 Elsevier B.V., All rights reserved.